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#intend to able to zoom into country when choosing the country,default singapore

Dataset

                 waste_type total_waste_generated_tonne
1 Construction & Demolition                     1624000
2            Ferrous Metals                     1269000
3           Paper/Cardboard                     1054000
4                  Plastics                      949000
5                      Food                      763000
6               Wood/Timber                      521000
  total_waste_recycled_tonne year total_waste_not_recycled_tonne recycling_rate
1                    1618000 2018                              6           1.00
2                     126000 2018                           1143           0.10
3                     586000 2018                            468           0.56
4                      41000 2018                            908           0.04
5                     126000 2018                            637           0.17
6                     428000 2018                             93           0.82
  wasting_rate
1         0.00
2         0.90
3         0.44
4         0.96
5         0.83
6         0.18
Shiny applications not supported in static R Markdown documents
Shiny applications not supported in static R Markdown documents

Chart B

# A tibble: 15 × 3
   Material                        Total Waste Generate…¹ Total Waste Recycled…²
   <chr>                                            <dbl>                  <dbl>
 1 Overall                                      131824000               75119000
 2 Paper/Cardboard                               23530000               11504000
 3 Ferrous Metals                                22414000               20472000
 4 Construction & Demolition                     22245000               21970000
 5 Plastics                                      15797000                1280000
 6 Food                                          13681000                1836000
 7 Wood/Timber                                    6608000                4440000
 8 Horticultural                                  5521000                3197000
 9 Others (stones, ceramic, rubbe…                5426000                 197000
10 Used Slag                                      5003000                4786000
11 Textile/Leather                                2854000                 199000
12 Non-Ferrous Metals                             2094000                1865000
13 Glass                                          1413000                 236000
14 Used slag                                      1366000                1347000
15 Scrap Tyres                                     502000                 431000
# ℹ abbreviated names: ¹​`Total Waste Generated (tonnes)`,
#   ²​`Total Waste Recycled (tonnes)`
# A tibble: 15 × 3
   waste_type                         total_waste_generated total_waste_recycled
   <chr>                                              <dbl>                <dbl>
 1 Overall                                        131824000             75119000
 2 Paper/Cardboard                                 23530000             11504000
 3 Ferrous Metals                                  22414000             20472000
 4 Construction & Demolition                       22245000             21970000
 5 Plastics                                        15797000              1280000
 6 Food                                            13681000              1836000
 7 Wood/Timber                                      6608000              4440000
 8 Horticultural                                    5521000              3197000
 9 Others (stones, ceramic, rubber, …               5426000               197000
10 Used Slag                                        5003000              4786000
11 Textile/Leather                                  2854000               199000
12 Non-Ferrous Metals                               2094000              1865000
13 Glass                                            1413000               236000
14 Used slag                                        1366000              1347000
15 Scrap Tyres                                       502000               431000

Chart C

Chart D

                 waste_type total_waste_generated_tonne
1 Construction & Demolition                     1624000
2 Construction & Demolition                     1440000
3 Construction & Demolition                      825000
4 Construction & Demolition                     1013000
5 Construction & Demolition                     1424000
6 Construction & Demolition                     1595000
  total_waste_recycled_tonne year total_waste_not_recycled_tonne recycling_rate
1                    1618000 2018                              6           1.00
2                    1434000 2019                              6           1.00
3                     822000 2020                              3           1.00
4                    1011000 2021                              2           1.00
5                    1419000 2022                              5           1.00
6                    1586000 2016                              9           0.99
  wasting_rate energy_saved crude_oil_saved
1         0.00        34644           96.00
2         0.00          252            0.72
3         0.00         1926            5.40
4         0.00        28000           80.00
5         0.00        20500           55.00
6         0.01         3600            9.90

======================================================================= ### Chart E

#```{r} #scatter plot graph fig <- plot_ly( data = total_data, x = ~year, y = ~energy_saved, size = ~total_waste_generate_tonne, color = ~material, sizes = c(10, 60), type = “scatter”, mode = “markers” )

fig

Energy saved per year

annual_energy_savings <- total_data %>% group_by(year) %>% summarize(energy_saved = sum(energy_saved))

Convert total_energy_saved to GWh

annual_energy_savings\(total_energy_saved <- round(annual_energy_savings\)energy_saved / 1000000, 2)

Display the result

tail(annual_energy_savings) ```